Since my under-graduate I have always been fascinated by the concept of causality. My first exposure to the concept was an argument between an atheist and a philosopher of faith. The latter insisted the existence of God can be explained logically. Every cause has an effect. The billiard ball when hit by another ball always moves. It can deduced inductively and deductively. It is empirically verifiable and an unquestionable fact. The atheist insisted such a logic of cause and effect cannot be applied to a mental construction that has been lingufied into the concept ‘God’. If God (Y) created the earth (X), then who created God. The cause can be infinitely regressed. Such was my introduction to complexity of causality.
My philosophical interest in causality has evolved into a social scientific interest. Or, more specifically into a policy focused concern (i.e. whether X amount of funding into pre-child care education will result in Y amount of children progressing on to third level education etc). However, the complexity of the concept still remains with me. The attraction of the concept is obvious; if you can, without doubt, determine that X caused Y then knowledge of X and Y increases substantially. In policy terms, evidence increases the cognitive persuasion of why we should adopt policy (a) over policy (b). If we know for a fact that providing free GP care (x) for all under 16’s increases life expectancy (y) then surely we should target resources into this health policy?
But, the problem is how do we know (beyond any reasonable doubt) that X causes Y? Causality is more often than not a complex interaction amongst a whole range of variables (diet, exercise, genetics, demographic, socio-economic et al). In natural science causality is much easier to establish. The change in the movement of the moon causes the tides to change. This is a fact. However, very few people ask what caused the moon? Such a regression would lead us back into the black hole of the big bang. Philosophically interesting but scientifically frustrating. So, why do people like me insist on asking what caused X1 to cause X2 to cause X3 to cause Y1? Probably because in the social world correlation is more the norm than causation in trying to explain Y1.
Causality can certainly be established in the social sciences. The methods for doing it are more complex because the phenomena under investigation are more complex. We can undoubtedly ascertain that an electoral system: PRSTV increases the number of political parties in the national legislature. We can safely say it is the ’cause’. But, it does not contain the same law like nature of a concept like gravity. At one stage we did not understand or know what caused things to stick to the earth. But, along came a scientist and through the use of experiments and inductive reasoning established the mental construct of gravity. It codified our understanding of his complex equation into a causal explanation. The social sciences do not have this natural luxury. To begin with, establishing social experiments are laced with problems, not least; who participates and why do they participate in a social experiment. Humans and social interaction (and the pursuit of collective interests) are simply more complex. Problems of representativeness also abound.
But, in quantitative public policy establishing whether X caused Y is extremely important because it is more often than not advising that policy makers should invest resources into X. This is the attraction of econometrics (economics-statistics-maths) in public policy. Hypothetically if I am given a budget of €1 million to increase participation in third level education in region of Dublin that has been classified as ‘disadvantaged’ then I am going to want to target the money to avoid waste. My objective is increase participation amongst the community in third level education (Y), therefore I will want to know what is the causal factor behind this community not going to third level. I could philosophise about the structural problems of capitalism but this futility will not solve the project I have been assigned.
So, I go about trying to identify the causal factors. After years of research with secondary school students I have narrowed it down to 5 variables; lack of information (x1), lack of motivation (x2), lack of family support (x3), early school leavers (x4), absence of career guidance (x5). However, further research by someone else leads me to conclude that the cognitive development of students in this community has been hampered by a whole variety of reasons at primary school level. The X’s increase to x15. Thus, how am I to know how to target this €1 million? I want the project to be a success and I want to ensure whatever I decide to do actually works. This is the complexity of causality for policy makers, and arguably, more relevant than the logical-abstract problem of causality as discussed by the atheist and priest in the opening paragraph.
Ultimately my regression analysis leads me down a black hole and I need to make a judgement informed by all the relevant and competing evidence. I decide to take a long term view and track the development of primary school kids by investing the money in pre-primary education for the kids and information workshops with parents. The effects may take 15 years, insufficient for the politician following the incentive of an electoral cycle every few years.
There is also a deeper normative question often left out of the policy debates over why we should invest money into X5 as this has been identified as the cause behind Y. Quantitative researchers have an unusually disliking of questions surrounding normativity. It is too subjective. But, all policy concerns have a normative dimension. Designing policy to increase access to third level education amongst marginal communities, or increasing life expectancy amongst children, or narrowing the income gap between the bottom and top earners in society is implicitly normative. It is motivated by questions of justice. We tend to want people to have education and good health as it increases overall societal well being. Human welfare matters. Even in pure economics there is a normative dimension. You may want more people educated to increase the size of the GDP pie. But, this itself is a value motive. Means and ends are intimately link in public policy. Making them explicit is more important than ignoring them.
But, ultimately the attraction of identifying whether X caused Y is the certainty it provides. Uncertainty is perhaps the norm in existence but establishing a path of certainty can surely lead to better choices. Either way, the complexity of causality will be central to all social scientific endevour.